






"""simple docstring"""


import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


__SCREAMING_SNAKE_CASE   =   logging.getLogger(__name__)

__SCREAMING_SNAKE_CASE   =   tf.data.AUTOTUNE


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :Optional[int]						=			argparse.ArgumentParser(description='Train a masked language model on TPU.' )
						parser.add_argument(
						    '--pretrained_model_config'     ,      type=a__     ,      default='roberta-base'     ,      help='The model config to use. Note that we don\'t copy the model\'s weights, only the config!'     ,      )
						parser.add_argument(
						    '--tokenizer'     ,      type=a__     ,      default='unigram-tokenizer-wikitext'     ,      help='The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'     ,      )

						parser.add_argument(
						    '--per_replica_batch_size'     ,      type=a__     ,      default=8     ,      help='Batch size per TPU core.'     ,      )

						parser.add_argument(
						    '--no_tpu'     ,      action='store_true'     ,      help='If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'     ,      )

						parser.add_argument(
						    '--tpu_name'     ,      type=a__     ,      help='Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'     ,      default='local'     ,      )

						parser.add_argument(
						    '--tpu_zone'     ,      type=a__     ,      help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'     ,      )

						parser.add_argument(
						    '--gcp_project'     ,      type=a__     ,      help='Google cloud project name. Only used for non-Colab TPU nodes.' )

						parser.add_argument(
						    '--bfloat16'     ,      action='store_true'     ,      help='Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'     ,      )

						parser.add_argument(
						    '--train_dataset'     ,      type=a__     ,      help='Path to training dataset to load. If the path begins with `gs://`'
						    ' then the dataset will be loaded from a Google Cloud Storage bucket.'     ,      )

						parser.add_argument(
						    '--shuffle_buffer_size'     ,      type=a__     ,      default=2**18     ,      help='Size of the shuffle buffer (in samples)'     ,      )

						parser.add_argument(
						    '--eval_dataset'     ,      type=a__     ,      help='Path to evaluation dataset to load. If the path begins with `gs://`'
						    ' then the dataset will be loaded from a Google Cloud Storage bucket.'     ,      )

						parser.add_argument(
						    '--num_epochs'     ,      type=a__     ,      default=1     ,      help='Number of epochs to train for.'     ,      )

						parser.add_argument(
						    '--learning_rate'     ,      type=a__     ,      default=1e-4     ,      help='Learning rate to use for training.'     ,      )

						parser.add_argument(
						    '--weight_decay_rate'     ,      type=a__     ,      default=1e-3     ,      help='Weight decay rate to use for training.'     ,      )

						parser.add_argument(
						    '--max_length'     ,      type=a__     ,      default=5_12     ,      help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'     ,      )

						parser.add_argument(
						    '--mlm_probability'     ,      type=a__     ,      default=0.15     ,      help='Fraction of tokens to mask during training.'     ,      )

						parser.add_argument('--output_dir'     ,      type=a__     ,      required=a__     ,      help='Path to save model checkpoints to.' )
						parser.add_argument('--hub_model_id'     ,      type=a__     ,      help='Model ID to upload to on the Hugging Face Hub.' )

						lowerCAmelCase   :str						=			parser.parse_args()
						return args


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						try:
												if args.tpu_name:
																		lowerCAmelCase   :Optional[Any]						=			tf.distribute.cluster_resolver.TPUClusterResolver(
																		    args.tpu_name     ,      zone=args.tpu_zone     ,      project=args.gcp_project )
												else:
																		lowerCAmelCase   :str						=			tf.distribute.cluster_resolver.TPUClusterResolver()
						except ValueError:
												raise RuntimeError(
												    'Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '
												    '--gcp_project. When running on a TPU VM, use --tpu_name local.' )

						tf.config.experimental_connect_to_cluster(a__ )
						tf.tpu.experimental.initialize_tpu_system(a__ )

						return tpu


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Dict						=			0
						for file in file_list:
												lowerCAmelCase   :str						=			file.split('/' )[-1]
												lowerCAmelCase   :List[Any]						=			re.search(R'-\d+-(\d+)\.tfrecord'     ,      a__ ).group(1 )
												lowerCAmelCase   :Optional[int]						=			int(a__ )
												num_samples += sample_count

						return num_samples


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__     ,      a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			count_samples(a__ )
						lowerCAmelCase   :Union[str, Any]						=			tf.data.Dataset.from_tensor_slices(a__ )
						if shuffle:
												lowerCAmelCase   :Any						=			dataset.shuffle(len(a__ ) )
						lowerCAmelCase   :Optional[int]						=			tf.data.TFRecordDataset(a__     ,      num_parallel_reads=a__ )
						# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
						lowerCAmelCase   :Union[str, Any]						=			dataset.apply(tf.data.experimental.assert_cardinality(a__ ) )
						lowerCAmelCase   :Optional[Any]						=			dataset.map(a__     ,      num_parallel_calls=a__ )
						if shuffle:
												assert shuffle_buffer_size is not None
												lowerCAmelCase   :Optional[Any]						=			dataset.shuffle(args.shuffle_buffer_size )
						lowerCAmelCase   :Optional[int]						=			dataset.batch(a__     ,      drop_remainder=a__ )
						lowerCAmelCase   :Tuple						=			dataset.map(a__     ,      num_parallel_calls=a__ )
						lowerCAmelCase   :str						=			dataset.prefetch(a__ )
						return dataset


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if not args.no_tpu:
												lowerCAmelCase   :Optional[int]						=			initialize_tpu(a__ )
												lowerCAmelCase   :Optional[Any]						=			tf.distribute.TPUStrategy(a__ )
						else:
												lowerCAmelCase   :List[Any]						=			tf.distribute.OneDeviceStrategy(device='/gpu:0' )

						if args.bfloataa:
												tf.keras.mixed_precision.set_global_policy('mixed_bfloat16' )

						lowerCAmelCase   :Any						=			AutoTokenizer.from_pretrained(args.tokenizer )
						lowerCAmelCase   :List[Any]						=			AutoConfig.from_pretrained(args.pretrained_model_config )
						lowerCAmelCase   :List[str]						=			tokenizer.vocab_size

						lowerCAmelCase   :Optional[Any]						=			tf.io.gfile.glob(os.path.join(args.train_dataset     ,      '*.tfrecord' ) )
						if not training_records:
												raise ValueError(F"""No .tfrecord files found in {args.train_dataset}.""" )
						lowerCAmelCase   :List[str]						=			tf.io.gfile.glob(os.path.join(args.eval_dataset     ,      '*.tfrecord' ) )
						if not eval_records:
												raise ValueError(F"""No .tfrecord files found in {args.eval_dataset}.""" )

						lowerCAmelCase   :Tuple						=			count_samples(a__ )

						lowerCAmelCase   :List[str]						=			num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
						lowerCAmelCase   :Any						=			steps_per_epoch * args.num_epochs

						with strategy.scope():
												lowerCAmelCase   :List[Any]						=			TFAutoModelForMaskedLM.from_config(a__ )
												model(model.dummy_inputs )  # Pass some dummy inputs through the model to ensure all the weights are built
												lowerCAmelCase	, lowerCAmelCase   :Dict						=			create_optimizer(
												    num_train_steps=a__     ,      num_warmup_steps=total_train_steps // 20     ,      init_lr=args.learning_rate     ,      weight_decay_rate=args.weight_decay_rate     ,      )

												# Transformers models compute the right loss for their task by default when labels are passed, and will
												# use this for training unless you specify your own loss function in compile().
												model.compile(optimizer=a__     ,      metrics=['accuracy'] )

						def decode_fn(a__ ):
												lowerCAmelCase   :List[Any]						=			{
												    'input_ids': tf.io.FixedLenFeature(dtype=tf.intaa     ,      shape=(args.max_length,) ),
												    'attention_mask': tf.io.FixedLenFeature(dtype=tf.intaa     ,      shape=(args.max_length,) ),
												}
												return tf.io.parse_single_example(a__     ,      a__ )

						# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
						# use their methods in our data pipeline.
						lowerCAmelCase   :Optional[Any]						=			DataCollatorForLanguageModeling(
						    tokenizer=a__     ,      mlm_probability=args.mlm_probability     ,      mlm=a__     ,      return_tensors='tf' )

						def mask_with_collator(a__ ):
												# TF really needs an isin() function
												lowerCAmelCase   :Optional[Any]						=			(
												    ~tf.cast(batch['attention_mask']     ,      tf.bool )
												    | (batch['input_ids'] == tokenizer.cls_token_id)
												    | (batch['input_ids'] == tokenizer.sep_token_id)
												)
												lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			data_collator.tf_mask_tokens(
												    batch['input_ids']     ,      vocab_size=len(a__ )     ,      mask_token_id=tokenizer.mask_token_id     ,      special_tokens_mask=a__     ,      )
												return batch

						lowerCAmelCase   :List[str]						=			args.per_replica_batch_size * strategy.num_replicas_in_sync

						lowerCAmelCase   :Optional[int]						=			prepare_dataset(
						    a__     ,      decode_fn=a__     ,      mask_fn=a__     ,      batch_size=a__     ,      shuffle=a__     ,      shuffle_buffer_size=args.shuffle_buffer_size     ,      )

						lowerCAmelCase   :Tuple						=			prepare_dataset(
						    a__     ,      decode_fn=a__     ,      mask_fn=a__     ,      batch_size=a__     ,      shuffle=a__     ,      )

						lowerCAmelCase   :Optional[int]						=			[]
						if args.hub_model_id:
												callbacks.append(
												    PushToHubCallback(output_dir=args.output_dir     ,      hub_model_id=args.hub_model_id     ,      tokenizer=a__ ) )

						model.fit(
						    a__     ,      validation_data=a__     ,      epochs=args.num_epochs     ,      callbacks=a__     ,      )

						model.save_pretrained(args.output_dir )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   parse_args()
		main(args)







"""simple docstring"""


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			MobileBertConfig.from_json_file(a__ )
						print(F"""Building PyTorch model from configuration: {config}""" )
						lowerCAmelCase   :Tuple						=			MobileBertForPreTraining(a__ )
						# Load weights from tf checkpoint
						lowerCAmelCase   :Any						=			load_tf_weights_in_mobilebert(a__     ,      a__     ,      a__ )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}""" )
						torch.save(model.state_dict()     ,      a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--mobilebert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained MobileBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)